Abstract
The recent resurgence of drug resistant strains of Mycobacterium tuberculosis , the causal agent of tuberculosis (TB) has strengthened the need for new anti-TB drugs. However, the accompanying experiments are lengthy due to the slow growth rate and pathogenic nature of the tubercle bacillus. Consequently a faster growing, non-pathogenic Mycobacterium would be ideal as a first screen in drug discovery. This project investigated metabolic features of M. smegmatis to establish its utility as a first screen in the discovery of metabolic drug targets. This thesis had two main areas of investigation: Firstly, the elucidation of the physiological and metabolic response of M. smegmatis at varying growth rates, under carbon-limitation in chemostat experiments. Secondly, the construction of genome scale metabolic networks (GSMN) of M. tuberculosis and M. smegmatis, with the purpose of consolidating biochemical information and carrying out in silico simulations. The accuracy of in silico predictions was assessed by comparison to in vitro experimental data. Results showed that the nature of the carbon source does not impact greatly on the macromolecular composition of M. smegmatis cultured at different growth rates. It was also shown that M. bovis BCG and M. sniegmatis possess different macromolecular compositions when grown under glycerol-limitation, and the macromolecular composition of each species responds differently to changes in growth rate. The biomass concentration of the two species responded in the same way to an increase in dilution rate, and in addition, it was seen that carbon-limited, continuously cultured mycobacterial growth cannot be described by Monod kinetics. The M. smegmatis GSMN predicted the effect of single gene deletions with an accuracy of 63%, whereas the GSMN of M. tuberculosis predicted global mutagenesis data with an accuracy of 78%. The M. smegmatis GSMN included more pathways involved in the metabolism of xenobiotics and it also permitted growth on methanol and carbon monoxide. In conclusion, this thesis contributes to the knowledge of M. smegmatis in the field of mycobacterial metabolism. It has implications for the use of GSMNs in drug discovery, mycobacterial phylogeny and comparative genomics.